Edwards S, Zhang R, Worley R, Mihaylova L, Aitken J, Anderson S R
Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom.
Front Robot AI. 2023 Mar 6;10:1150508. doi: 10.3389/frobt.2023.1150508. eCollection 2023.
Buried sewer pipe networks present many challenges for robot localization systems, which require non-standard solutions due to the unique nature of these environments: they cannot receive signals from global positioning systems (GPS) and can also lack visual features necessary for standard visual odometry algorithms. In this paper, we exploit the fact that pipe joints are equally spaced and develop a robot localization method based on pipe joint detection that operates in one degree-of-freedom along the pipe length. Pipe joints are detected in visual images from an on-board forward facing (electro-optical) camera using a bag-of-keypoints visual categorization algorithm, which is trained offline by unsupervised learning from images of sewer pipe joints. We augment the pipe joint detection algorithm with drift correction using vision-based manhole recognition. We evaluated the approach using real-world data recorded from three sewer pipes (of lengths 30, 50 and 90 m) and benchmarked against a standard method for visual odometry (ORB-SLAM3), which demonstrated that our proposed method operates more robustly and accurately in these feature-sparse pipes: ORB-SLAM3 completely failed on one tested pipe due to a lack of visual features and gave a mean absolute error in localization of approximately 12%-20% on the other pipes (and regularly lost track of features, having to re-initialize multiple times), whilst our method worked successfully on all tested pipes and gave a mean absolute error in localization of approximately 2%-4%. In summary, our results highlight an important trade-off between modern visual odometry algorithms that have potentially high precision and estimate full six degree-of-freedom pose but are potentially fragile in feature sparse pipes, simpler, approximate localization methods that operate in one degree-of-freedom along the pipe length that are more robust and can lead to substantial improvements in accuracy.
地下污水管网给机器人定位系统带来了诸多挑战，由于这些环境的独特性质，需要非标准解决方案：它们无法接收全球定位系统（GPS）的信号，而且可能缺乏标准视觉里程计算法所需的视觉特征。在本文中，我们利用管接头等距分布这一事实，开发了一种基于管接头检测的机器人定位方法，该方法沿管道长度在一个自由度上运行。使用关键点袋视觉分类算法在车载前向（电光）相机拍摄的视觉图像中检测管接头，该算法通过对污水管接头图像进行无监督学习离线训练。我们使用基于视觉的人孔识别对管接头检测算法进行漂移校正。我们使用从三根污水管（长度分别为30米、50米和90米）记录的真实数据对该方法进行了评估，并与视觉里程计的标准方法（ORB-SLAM3）进行了基准测试，结果表明我们提出的方法在这些特征稀疏的管道中运行更稳健、更准确：由于缺乏视觉特征，ORB-SLAM3在一根测试管道上完全失败，在其他管道上定位的平均绝对误差约为12%-20%（并且经常丢失特征跟踪，不得不多次重新初始化），而我们的方法在所有测试管道上均成功运行，定位的平均绝对误差约为2%-4%。总之，我们的结果突出了现代视觉里程计算法之间的一个重要权衡，即这些算法可能具有高精度并能估计完整的六自由度姿态，但在特征稀疏的管道中可能很脆弱，而更简单的、沿管道长度在一个自由度上运行的近似定位方法更稳健，并且可以大幅提高精度。
